Exploring the mechanism of active components from ginseng to manage diabetes mellitus based on network pharmacology and molecular docking

A large body of literature has shown that ginseng had a role in diabetes mellitus management. Ginsenosides are the main active components of ginseng. But what ginsenosides can manage in diabetic are not systematic. The targets of these ginsenosides are still incomplete. Our aim was to identify which ginsenosides can manage diabetes mellitus through network pharmacology and molecular docking. To identify the targets of these ginsenosides. In this work, we retrieved and screened ginsenosides and corresponding diabetes mellitus targets across multiple databases. PPI networks of the genes were constructed using STRING, and the core targets were screened out through topological analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed by using the R language. Finally, molecular docking was performed after bioinformatics analysis for verification. Our research results showed that 28 ginsenosides in ginseng might be against diabetes mellitus by modulating related proteins such as VEGFA, Caspase 3, and TNF-α. Among the 28 ginsenosides, 20(R)-Protopanaxatriol, 20(R)-Protopanaxadiol, and Ginsenoside Rg1 might play a significant role. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analysis showed that the management of diabetes mellitus by ginsenosides may be related to the positive regulation of reactive oxygen metabolic processes, associated with the insulin signaling pathway, TNF signaling pathway, and AMPK signaling pathway. Molecular docking results and molecular dynamics simulation showed that most ginsenosides could stably bind to the core target, mainly hydrogen bonding and hydrophobic bond. This study suggests the management of ginseng on diabetes mellitus. We believe that our results can contribute to the systematic study of the mechanism of ginsenosides for the management of diabetes mellitus. At the same time, it can provide a theoretical basis for subsequent studies on the management of ginsenosides in diabetes mellitus.

RCSB PDB database (https:// www. rcsb. org/). The 2D structures of active substances from ginseng were sought by the PubChem Database 23 . The ChemBio Draw 3D (https:// www. chemd raw. com. cn/ ChemB io3D. html) and Autodock Tool was used to optimise the structure of ginsenosides and receptor proteins and to convert the two formats 24 . PyMol (https:// pymol. org/2/) was used to optimize receptor proteins 25 . We used Chem3D software to minimize the energy of the ligand. We used Pymol software to remove water and increase hydrogen bonds in the acceptor protein. Finally, AutDockTools were used to draw the grid and unify. Autodock Vina was used for molecular docking to obtain the conformation with the lowest binding energy during docking. In addition, we used standard molecules and core targets for molecular docking and molecular dynamics analysis. The same binding pocket of protein molecules was used in the docking process. After, the lowest energy binding profile was visualized. Discovery Studio and PyMol software were used to visualize docking results 26 .

Molecular dynamics simulation.
After the docking studies were completed, the active ingredients with the good binding ability to the protein were further evaluated for their stability in the binding pocket using the amber18 software package to run 100 ns molecular dynamics of protein-compound conjugates. The ff14SB force field and the TIP3P water model were performed at a constant temperature and pressure and a periodic boundary condition. The force field of the micro-molecule was generated using the antechamber in AmberTools. During molecular dynamics simulation, the hydrogen bond involved was constrained using the LINCS algorithm with 2.0 fs integration time step, and then the Particle Mesh Ewald method was applied to calculate the electrostatic interactions with the cutoff value set to 1.2 nm. The non-bonding interaction cutoff was 10 Å and was updated every 10 steps. In addition, the V-rescale temperature coupling and Berendsen method were used to keep the temperature and pressure constant at 300 K and 1 bar, respectively, to perform 100 ps NVT and NPT equilibrium simulation. Molecular dynamics simulations of 100 ns were performed under NPT conditions 27 . Investigation of binding affinity using molecular mechanics Poisson-Boltzmann surface area (MM-PBSA). The relative binding energy of a protein-ligand complex is popularly used in MD simulations and thermodynamic computations. MM-PBSA in combination with MD simulations is used to calculate the binding energy of protein and ligand complexes using the equation ΔG (Binding) = G (Complex)-G (Receptor)-G (Ligand) , where "G (complex) is total free energy of the ligand-protein complex, G (receptor) and G (ligand) are total free energies of the isolated protein and ligand in the solvent, respectively. The molecular mechanic's potential energy plus the energy of solvation may be used to determine the total free energy of any of the three entities stated (ligand, receptor, or complex). We used the "amber-mmpbsa module" tool to perform binding energy calculations. The stable trajectory observed between the 90-100 ns was chosen for the binding energy calculation by selecting representative snapshots with an interval of 50 frames 28 .

Results
Active ingredients' screening and collection of DM-relevant targets. We obtained 28 ginsenosides through the TCMSP database combined with literature supplementation. These components and their information were presented in Table 1. After analyzing the data, we found that PPT, Ginsenoside Rg1 (Rg1), 20(S)-Ginsenoside Rh1 (Rh1), 20(S)-Ginsenoside Rh2 (Rh2), Ginsenoside Rh4 (Rh4), Ginsenoside Ro (Ro), and 20(R)-Protopanaxadiol (PPD) had more targets than other ginsenosides. Integrating five disease databases yielded 4341 DM-related targets as shown in Fig. 1A. The targets information of the ingredients and disease were in Supplementary Tables 1 and 2. The 101 intersection targets were obtained after integration, and a Veen diagram was drawn as shown in Fig. 1B. The specific intersection targets information were presented in Supplementary  Table 3. Component-target network diagram for a more comprehensive elucidation of the mechanism of action www.nature.com/scientificreports/ of ginseng in DM by Cytoscape 3.7.0. In Fig. 1C, the blue diamond represents the components in the ginseng, and the pink ovals represent the targets. We found that ginsenoside PPT, Rg1, Rh1, Rh2, Rh4, Ro, and PPD had more targets for linkage compared with other ginsenosides. The results showed that each ginsenoside can act on multiple targets, and each target can also respond to multiple ginsenosides. This is consistent with the traditional Chinese medicine theory that the herbal treatment process is designed to be multi-component and multi-target.
Construction and analysis of the PPI network. The core target network diagram was analyzed using Cytoscape 3.7.0. The results showed that 99 interacting target proteins were obtained by screening the String database. The core target network graph was composed of 99 nodes and 829 edges. Interactions were represented by each edge between proteins. The network analyzers identified 9 highly connected nodes (BC > 165.2336, CC > 0.5715, DC > 33, EC > 0.1695, LAC > 17.2749, and NC > 26.3852) as significantly core targets. A total of 99 protein-protein intersection targets were obtained and visualized in the PPI network ( Fig. 2A). Figure 2B shown the minor core protein. As depicted in Fig. 2C, 9 core targets (IL-1β, EGFR, CASP3, TNF-α, VEGFA, STAT3, JUN, mTOR, EGRF, MAPK1) were screened out by sing Cytoscape 3.7.0. The names and details of the nine core targets were presented in Table 2. After comparison with existing reports, these targets played a major role in the development of DM.
GO and KEGG pathway enrichment analysis. GO functional enrichment analysis of key targets was performed using the Bioconductor package in R software. Filter GO terms based on P values (P < 0.05, Q < 0.05). There were 2029, 56, and 153 GO terms associated with BP, cellular components, and molecular functions, respectively (Fig. 3A). As shown in Fig. 4A, the top 10 BP were related to positive regulation of protein serine/ threonine kinase (STK) activity, positive regulation of MAP kinase activity, positive regulation of reactive oxygen species metabolic process, and regulation of lipid metabolic process, response to a steroid hormone, etc. In terms of cellular components, targets were enriched in the mitochondrial outer membrane, ATPase dependent transmembrane transport complex. Functional analysis showed steroid hormone receptor activity, protein STK activity, glucose transmembrane transporter activity, monosaccharide transmembrane transporter activity, etc. The component-target-BP network interaction diagram. From left to right in the picture were the ingredient information, target information, and BP information (Fig. 3B). The GO enrichment analysis information and 15 selected GO were provided in Supplementary Tables 4 and 5.
The KEGG involved 169 signaling pathways. These signaling pathways were the AGE-RAGE, AMPK, VEGF, Insulin, Adipocytokine, and PI3K-Akt signaling pathway (Fig. 3C). Figure 3D showed the  www.nature.com/scientificreports/  www.nature.com/scientificreports/ component-target-signaling pathway network interaction diagram. From left to right in the picture were ingredient information, target information, and signaling pathway information. The KEGG enrichment analysis information and 15 selected KEGG were provided in Supplementary Tables 6 and 7. Combined with the previous reports, we speculated that VEGF, Insulin, B cell receptor, and TNF signaling pathway might play an important role in the management of DM by ginseng ( Fig. 4). At the same time, we obtained an open access license for KEGG signaling pathway images by Kanehisa laboratories (Supplementary Fig. 1). Fifteen BPs and signaling pathways were involved in 71 and 43 targets in that order. From the reciprocal network in Figs. 4B and D, we can know that each ginsenoside can act on different targets and these target proteins had a presence in BP or signaling pathways. It indicated that ginsenosides could play a role in regulating BP or signaling pathways. The results of the component-target network, component-target-BP, or signaling pathway network showed that PPT, Rh1, Rh2, Rh4, Rg1, Ro, and PPD had more complex networks compared to other ginsenosides.
Ginsenosides and core target molecular docking results. We treated the top 9 scoring targets with each of the 28 ginsenosides for molecular docking. The docking protein data were sequentially downloaded from the PDB database IL-1β, EGFR, Caspase 3, VEGFA, STAT3, mTOR, MAPK1, TNF-α and JUN PDB format files. The screened 28 ginsenosides were downloaded from the PubChem database. The download format was an SDF file. AutoDock software was used to molecularly dock small molecule active ligands to large molecule receptor proteins. The grid box size and center details' information were in Supplementary Table 8. We used the AutoDock Vina software to calculate the energy binding of the components to the receptor protein (Table 3). Among all the molecular docking results, the maximum binding energy was − 6.4 kcal/mol, and the minimum binding energy was − 10 kcal/mol. Moreover, 89.2% of the binding energy results were less than − 7.0 kcal/ mol. When a small molecule ligand binds to a large molecule protein, the lower the binding energy indicates the more stable the two were bound. In addition, by comparing the docking and MD results, we found that Ginsenoside Rg1, Ginsenoside Rh4 and Protopanaxatriol had lower binding energies and better stability than the standard receptor proteins. This result further demonstrates the ability of ginsenosides to manage in diabetes.
(Supplementary Table 9 and Supplementary Fig. 2). Some of the results were visualized with Discovery Studio and PyMol software. Figure 5A, B shown the interaction modes of ginsenosides with JUN receptor protein: PPD interacts with the residues ANS 602, and LYS 666 using hydrogen bonding, with the residue GLN 601, PRO 635, PHE 603, THR 604, GLU 606, GLU 576, ASN 662 via van der Waals interactions forces, with the residue HIS 575 via hydrophobicity in the form Ginsenoside Rg1 www.nature.com/scientificreports/ of a π bond interactions forces, and with the residue HIS 575 through a π Sigma effect. This process involved two hydrogen bonds and two hydrophobic bonds. The interaction modes of PPT with mTOR receptor protein: PPD interacts with the residues GLN 2115, TYR 2075, SER 2113, ILE 2112, ASP 2103 by way of van der Waals interactions forces, with the residues LYS 2114, ARG 2110, HIS 2107, TYR 2106 ARG 2110 via hydrophobicity and hydrophobicity in the form of a π bond interactions forces, with the residues ARG 2110 by means of a conventional hydrogen bond. One hydrogen bond and twelve hydrophobic bonds were involved in this process (Fig. 5C, D). The interaction modes of ginsenoside Rg1 with TNFα receptor protein: ginsenoside Rg1 interacts with the residues CYS 69, LYS 65, ASP 140 by means of hydrogen bonding, with the residues ALA 111, GLU 110, GLY 68, PRO 70, THR 105, PRO 106, GLN 67, GLY 66 via van der Waals interactions forces, with the residues PRO 70 via a carbon-hydrogen bond, with the residueTYR 141 via hydrophobicity in the form of a π bond interactions forces. This process involved four hydrogen bonds and two hydrophobic bonds (Fig. 5E, F) (Fig. 5K, L).
The dynamics stability simulation of ginsenosides with core receptor proteins. We selected ginsenosides with more DM-related targets, and the selected receptor proteins were at the core of the network analysis. On this basis, we carry out the following molecular dynamics analysis. The ginsenosides-receptor protein was further selected to perform a computer molecular dynamics simulation study in the hope of www.nature.com/scientificreports/ understanding their stability in the binding pocket. Of note, the root means square deviation (RMSD) was an important basis to measure whether the system was stable or not. The mTOR-Protopanaxatriol, STAT3-Ginsenoside Rh4, TNFα-Ginsenoside Rg1, and IL-1β-Ginsenoside Rh2 systems were consistently lower fluctuation throughout the molecular dynamics simulations. Their average RMSD value were 0.21299 ± 0.00016 nm, 0.24917 ± 0.00021 nm, 0.28859 ± 0.00019 nm, and 0.34124 ± 0.00030 nm. The JUN-Protopanaxadiol and VEGFA-Ginsenoside Rh1 systems fluctuated slightly throughout the molecular dynamics simulations. Their average RMSD value were 0.65815 ± 0.00115 nm. The protopanaxadiol was unstable against JUN. But may be suitable drug candidate for JUN. (Fig. 6A). Next, the flexible change in amino acid residues in receptor protein and the root mean square fluctuation (RMSF) value were evaluated. We sequentially evaluated the average RMSF of mTOR-Protopanaxatriol, STAT3-Ginsenoside Rh4, TNFα-Ginsenoside Rg1, IL-1β-Ginsenoside Rh2, JUN-Protopanaxadiol, and VEGFA-Ginsenoside Rh1 system. Their average RMSF value were 0.06883 ± 0.00017 nm, 0.10717 ± 0.0039 nm, 0.10926 ± 0.00621 nm, 0.11252 ± 0.00580 nm, 0.22683 ± 0.00565 nm, and 0.14594 ± 0.00923 nm. The fluctuation frequency of the above six systems is low. Among them, the mTOR-Protopanaxatriol system had the least fluctuation (Fig. 6B). Taken together, all the results confirmed the stability of ginsenosides and core receptor proteins during molecular dynamics simulation. Among the six systems, mTOR-Protopanaxatriol and IL-1β-Ginsenoside Rh2 had the lowest mean Rg values of 1.31215 ± 0.00683 nm and 1.50815 ± 0.00893 nm respectively indicating that these two systems were the most stable. TNF-α-Ginsenoside Rg1, VEGFA-Ginsenoside Rh1, and STAT3-Ginsenoside Rh4 systems were more stable with Rg values of 1.72798 ± 0.015499 nm, 1.79617 ± 0.02037 nm, and 1.94129 ± 0.01815 nm. Jun-Protopanaxadiol was the most fluctuating among the six systems, and its Rg value of 2.03092 ± 0.05526 nm was more unstable (Fig. 6C). The solvent-accessible surface area (SASA) was analyzed to distinguish the protein compactness behavior. The results showed that the average values of mTOR-Protopanaxatriol, JUN-Protopanaxadiol,TNF-α-ginsenoside Rg1, VEGFA-ginsenoside Rh1, IL-1β-ginsenoside Rh2, STAT3ginsenoside Rh4 were 58.76678 ± 0.01051nm 2 , in order of 151.97051 ± 0.02124 nm 2 , 82.37012 ± 0.01051, 67 nm 2 , 96,321 ± 0.01178 nm 2 , 76.50828 ± 0.01791 nm 2 , and 101.14167 ± 0.02517 nm 2 (Fig. 6D). The H-bond interaction for each complex was shown in Fig. 7. The most involved hydrogen bonds during molecular dynamics simulations were IL-1β-Ginsenoside Rh2 (11,454), followed by VEGFA-Ginsenoside Rh1 (10,363), TNF-α-Ginsenoside Rg1 The results of the binding free energy analysis shown that the van der Waal energy between mTOR and Protopanaxatriol was − 30.9954 kcal/mol, the electrostatic energy was − 7.4076 kcal/mol, the polar solvation energywas 17.1705 kcal/mol, the non-polar solvation energywas energy was − 3.8093 kcal/mol, and the total binding energy was − 25.0419 kcal/mol (Fig. 9A). The results of the binding free energy analysis shown that the van der Waal energy between JUN and Protopanaxadiol was − 19.2009 kcal/mol, the electrostatic energy was − 9.9003 kcal/mol, the polar solvation energywas 16.7755 kcal/mol, the non-polar solvation energywas energy was − 2.8325 kcal/mol, and the total binding energy was − 15.1582 kcal/mol (Fig. 9B). The results of the binding free energy analysis shown that the van der Waal energy between TNF-α and Ginsenoside Rg1 was − 1.8493 kcal/ mol, the electrostatic energy was − 1.8218 kcal/mol, the polar solvation energywas 3.4406 kcal/mol, the nonpolar solvation energywas energy was − 0.2152 kcal/mol, and the total binding energy was − 0.4458 kcal/mol (Fig. 9C). The results of the binding free energy analysis shown that the van der Waal energy between VEGFA and Ginsenoside Rh1 was − 6.0465 kcal/mol, the electrostatic energy was − 9.5102 kcal/mol, the polar solvation energywas 13.1659 kcal/mol, the non-polar solvation energywas energy was − 0.9032 kcal/mol, and the total binding energy was − 3.294 kcal/mol (Fig. 9D). The results of the binding free energy analysis shown that the van der Waal energy between IL-1β and Ginsenoside Rh2 was − 29.9513 kcal/mol, the electrostatic energy was − 3.7712 kcal/mol, the polar solvation energywas 14.6004 kcal/mol, the non-polar solvation energywas energy was − 3.5329 kcal/mol, and the total binding energy was − 22.655 kcal/mol (Fig. 9E). The results of the binding free energy analysis shown that the van der Waal energy between STAT3 and Ginsenoside Rh4 was − 23.1377 kcal/ mol, the electrostatic energy was − 10.4156 kcal/mol, the polar solvation energywas 20.9973 kcal/mol, the nonpolar solvation energywas energy was − 2.8245 kcal/mol, and the total binding energy was − 15.3806 kcal/mol (Fig. 9F). Among the six systems, the mTOR-Protopanaxatriol and IL-1β -Ginsenoside Rh2 systems had the lowest total binding energy and the most stable binding. www.nature.com/scientificreports/

Discussion
DM remains an insurmountable problem worldwide 29 . Chinese herbal medicine has been handed down in China for thousands of years and has a deep historical heritage. A large number of ancient texts on Chinese medicine have clearly documented the effects of herbs to manage DM, such as ginseng 30,31 .
There is increasing evidence that ginsenosides have anti-diabetic and insulin-sensitizing properties. At the same time, ginsenosides not only have the effects of lowering blood sugar, managing insulin sensitivity, and regulating lipid metabolism but also can alleviate the occurrence of DM 32,33 .
Our group conducted some research on ginsenosides to manage DM. The ginsenoside 20(S)-Ginsenoside Rg3 had a protective effect on animal model DM by regulating the MAPK/NF-κB signaling pathway 34 . Rh1 ameliorates DM through AMPK/PI3K/Akt-mediated inflammatory and apoptosis signaling pathways 12 . The malonyl ginsenosides significantly reduced the fasting blood glucose, triglyceride, total cholesterol, low-density lipoprotein cholesterol levels, etc., and managed glucose tolerance and insulin resistance 35,36 . The results showed that the hypoglycemic and insulin-sensitizing capabilities of Compound K (CK) on type 2 DM induced by HFD/ STZ via down-regulation of PEPCK and G6Pase expression in the liver 37 . We found that CK could provide beneficial anti-diabetic effects in DM mice, and this protective effect may be mediated by preventing β-cell apoptosis by inhibiting the AMPK-JNK pathway 38 . The predicted results of the present work almost coincide with the findings of our group.
The results of KEGG signalling pathway enrichment analysis indicated that the above signalling pathway might be a significant signalling pathway for ginsenosides to manage DM. However, whether the unreported saponins in ginseng can manage DM mellitus and its mechanism of action are still unclear. Therefore, this study aimed to investigate the managing effects of ginsenosides on DM and their possible mechanisms of action based on network pharmacology and the molecular docking methodology.
As seen above, ginsenosides, as the main active ingredient, can be effective against DM. These previous reports provide sufficient support for our next step to explore the molecular mechanism of components obtained from ginseng to manage DM. In this work, the ginsenosides and their action targets were collected by data mining. In the Venn diagram of the intersection of components in ginseng and DM, 99 cross targets of protein-protein, and 9 core targets were screened by Cytoscape 3.7.0 software. VEGFA plays a major role in endothelial cell growth and angiogenesis and is an essential factor. There were considerable evidence that when DM occurs it often lead to overexpression of VEGFA, which can have a detrimental effect on the body 39,40 . TNF is an important proinflammatory factor that can manage DM by reducing insulin signaling through phosphorylation of serine 41 . Moreover, TNF-α is closely associated with diabetic nephropathy and vascular dysfunction 42 . www.nature.com/scientificreports/ The results of BP results showed that the ginsenosides were related to the process of positive regulation of protein STK activity, positive regulation of MAP kinase activity, regulation of lipid metabolic process, and positive regulation of reactive oxygen species metabolic process, etc. A lot of evidence has shown that oxidative stress damage leads to lipid peroxidation. Lipid peroxidation causes the rearrangement of peroxyl radicals which can lead to a large of pathological changes in the body. The occurrence of DM often leads to an abnormally high expression of reactive oxygen species (ROS). This can accelerate the onset of cardiovascular disease in DM. Many studies have confirmed that during the development of DM, a shift in glycolytic metabolism occurs, which lead to an overproduction of ROS in monocytes and macrophages. Furthermore, macrophages were produced in large numbers when DM occurs, releasing excessive amounts of pro-inflammatory factors and proteases that lead to inflammation. Because ROS were important mediators in the activation of pro-inflammatory signaling pathways, obesity-and hyperglycemia-induced ROS overproduction may favor the induction of M1-like pro-inflammatory macrophages during the onset and progression of DM 43,44 . This will lead to further deterioration of the disease. These conditions increased the likelihood of cardiovascular disease in people with DM. When a patient suffers from concomitant cardiovascular disease, it was not conducive to the management of DM, forming a vicious circle 45 . Fortunately, we found that ginsenosides may have a certain regulatory effect on the regulation of blood vessel diameter and vascular process in the circulatory system. When DM occurs, the body's skin healing ability is greatly reduced 46 . BP results show that ginsenosides have a regulatory effect on epithelial cell proliferation. This BP may manage DM-induced skin healing difficulties.
These BP were mainly closely related to the molecular functions of steroid binding, steroid hormone receptor activity, and adrenergic receptor activity. And that mainly occur in the mitochondrial outer membrane, an integral component of the presynaptic membrane, neuronal cell body, etc.
Ginsenosides might be involved in a wide range of BP in managing DM and its complications, and these BP are closely related to a variety of molecular functions and cellular components. Overall, our GO results clearly indicated that ginseng could be used to manage DM by modulating BP to ultimately manage it.
A lot of evidence shown that when DM occurs, it tends to cause a series of symptoms such as obesity, insufficient insulin secretion, inflammation, elevated cholesterol, and hardened blood vessels. Our KEGG enrichment analysis showed that insulin, AGE-RAGE, TNF, AMPK, VEGF, adipocyte factor, HIF-1, and PI3K-Akt signaling pathway might be the major signaling pathways 12,38 . The HIF-1 pathway was a well-known regulator of cellular glucose 47 . When diabetics were exposed to a hypoxic environment, HIF was activated and this led to the release of large amounts of inflammatory factors from damaged organs 48,49 . Diabetic patients often had a condition with www.nature.com/scientificreports/ elevated pro-inflammatory factors, such as TNF-α. TNF-α mediated inflammation, obesity, and insulin resistance were associated with DM 50 . It had been reported that the PI3K/AKT signalling pathway is activated when HKC cells were exposed to a high glucose environment. This promoted islet cell proliferation 51 . Ultimately it was beneficial for DM to manage. Overall, our results suggested that ginsenosides could act on multiple targets and in multiple pathways. Our findings showed that ginsenosides might regulate the above signaling pathways by acting on Caspase 3, MTOR, VEGFA, MAPK1, JUN, TNF-α, STAT3, IL-1β, EGFR and other targets, thereby managing DM 52,53 . When the binding energy of the receptor protein and the small molecule fraction is less than 0 kcal/mol, it indicates that the two can bind together, and when the binding energy is less than − 7.0 kcal/mol, it indicates that the two can bind together more stably 19 . Our results show 224 molecular dockings with binding energies less than − 7.0 kmol/mol, which accounts for 89.2% of all docking results. This showed that the ginsenosides in ginseng could bind well to the nine core genes. Ginsenosides bind to core targets in a variety of ways, mainly hydrogen bonds, as we know that the binding energy of molecular docking is determined by both the receptor protein and the small molecule ligand. However, it is not clear which of the two has a greater impact on binding energy. In this part of the results, we found that receptor proteins had the greatest effect on the binding energy. The conformation and relative molecular mass of small molecules such as S-or R-type receptors had no significant effect on the binding energy. Although further investigation is needed to obtain more reliable conclusions, these results give us an important hint. Combined with the results of molecular docking implied that ginsenosides have some targeting to the receptor protein. To some extent, it can explain that ginsenosides can manage DM with certain targeting properties.
Furthermore, molecular dynamics simulation, one of the effective tools used to check the stability of the protein ligand complex, was applied to assess the stability of ginsenosides to receptor proteins in the binding pocket in 100 ns. Interestingly, the molecular dynamics simulation results were in agreement with the molecular docking results and suggest the favorable stability along with proper interaction during the time, for both bioactive ingredients with the receptor proteins. The MM-PBSA analysis further clarified the major amino acid sites of different targets and their contribution values. The composition of free energy of ginsenosides and targets during the binding process was also defined clearly. These results further provide that ginsenosides have activity in the management of diabetes mellitus.
So far, more than 100 ginsenosides have been identified. Our prediction results indicated that 28 saponins had an anti-diabetic effect 54 . The predicted amount of saponins accounted for about one fourth of the total. The remaining unpredicted ginsenosides may also have anti-diabetic effects. The reason for this could be the low OB and DL of other ginsenosides or their unclear target. However, compared with the existing reports, our predicted ginsenosides are relatively comprehensive and the mechanism of action is relatively systematic. Meanwhile, ginseng polysaccharide components have been shown to manage DM, but due to some limitations, these components could not be analyzed by network pharmacology 55 . This type of active ingredient still needs further research. Overall, the application of network pharmacology allowed the mechanisms of action of traditional Chinese medicine in the treatment of disease to be explored at the molecular level. It was more systematic to provide valid evidence and basis for the management of DM by ginseng. However, there were shortcomings, such as lesser reported herbs not being searchable, less comprehensive targets for some ingredients, and less software available to correspond to them.
In conclusion, our findings clearly indicated that the managed results of 28 ginsenosides in ginseng on DM were through a multi-component, multi-target, multi-pathway overall regulation. The process involved 99 relevant targets, 2238 GO terms and 169 signaling pathways. This result was consistent with traditional chinese medicine theory. Among the 28 ginsenosides, PPT, Rh1, Rh2, Rh4, Ro, Rg1, and PPD interacted with more targets, BP, and signaling pathways. We speculated that these seven saponins might play a significant role in managing DM. A comparison with the literature, molecular docking, and molecular dynamics simulation showed the authenticity and reliability of our results.